Extracting Core Elements of TFM Functional Characteristics from Stanford CoreNLP Application Outcomes

Abstract

Stanford CoreNLP is the Natural Language Processing (NLP) pipeline that allow analysing text at paragraph, sentence and word levels. Its outcomes can be used for extracting core elements of functional characteristics of the Topological Functioning Model (TFM). The TFM elements form the core of the knowledge model kept in the knowledge base. The knowledge model ought to be the core source for further model transformations up to source code. This paper presents research on main steps of processing Stanford CoreNLP application results to extract actions, objects, results and executors of the functional characteristics. The obtained results illustrate that such processing can be useful, however, requires text with rigour, and even uniform, structure of sentences as well as attention to the possible parsing errors.